Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 KiB
Average record size in memory520.3 B

Variable types

Numeric24
Text1
Categorical5

Alerts

MO (baseline) is highly overall correlated with MO Day7High correlation
MO Day1 is highly overall correlated with MO Day2High correlation
MO Day2 is highly overall correlated with MO Day1 and 1 other fieldsHigh correlation
MO Day7 is highly overall correlated with MO (baseline) and 1 other fieldsHigh correlation
S1 (baseline) is highly overall correlated with S1 day1 and 4 other fieldsHigh correlation
S1 day1 is highly overall correlated with S1 (baseline) and 4 other fieldsHigh correlation
S1 day2 is highly overall correlated with S1 (baseline) and 6 other fieldsHigh correlation
S1 day7 is highly overall correlated with S1 (baseline) and 4 other fieldsHigh correlation
S2 (baseline) is highly overall correlated with S1 (baseline) and 9 other fieldsHigh correlation
S2 day1 is highly overall correlated with S1 day1 and 8 other fieldsHigh correlation
S2 day2 is highly overall correlated with S1 day1 and 8 other fieldsHigh correlation
S2 day7 is highly overall correlated with S1 (baseline) and 9 other fieldsHigh correlation
S3 (baseline) is highly overall correlated with S2 (baseline) and 6 other fieldsHigh correlation
S3 day1 is highly overall correlated with S2 (baseline) and 6 other fieldsHigh correlation
S3 day2 is highly overall correlated with S2 (baseline) and 6 other fieldsHigh correlation
S3 day7 is highly overall correlated with S2 (baseline) and 6 other fieldsHigh correlation
VAS (baseline) is highly overall correlated with VAS Day1High correlation
VAS Day1 is highly overall correlated with VAS (baseline) and 1 other fieldsHigh correlation
VAS Day7 is highly overall correlated with VAS day2 High correlation
VAS day2 is highly overall correlated with VAS Day1 and 1 other fieldsHigh correlation
Impaction is highly imbalanced (59.9%) Imbalance
Subject is uniformly distributed Uniform
Subject has unique values Unique
Name has unique values Unique
VAS (baseline) has 10 (14.9%) zeros Zeros
VAS Day1 has 6 (9.0%) zeros Zeros
VAS day2 has 9 (13.4%) zeros Zeros
VAS Day7 has 30 (44.8%) zeros Zeros
Arcoxia (days) has 14 (20.9%) zeros Zeros

Reproduction

Analysis started2025-05-19 03:26:58.815562
Analysis finished2025-05-19 03:28:04.379141
Duration1 minute and 5.56 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Subject
Real number (ℝ)

Uniform  Unique 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:04.474147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q117.5
median34
Q350.5
95-th percentile63.7
Maximum67
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.485037
Coefficient of variation (CV)0.57308932
Kurtosis-1.2
Mean34
Median Absolute Deviation (MAD)17
Skewness0
Sum2278
Variance379.66667
MonotonicityStrictly increasing
2025-05-19T03:28:04.634481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
51 1
 
1.5%
37 1
 
1.5%
38 1
 
1.5%
39 1
 
1.5%
40 1
 
1.5%
41 1
 
1.5%
42 1
 
1.5%
43 1
 
1.5%
44 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%

Name
Text

Unique 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2025-05-19T03:28:04.921860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length14.044776
Min length5

Characters and Unicode

Total characters941
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)100.0%

Sample

1st rowMuhammad Amerul Akmal
2nd rowSarah Nabihah
3rd rowPan Anyu
4th rowAfiqah bitni Abdul Malik
5th rowTiang Nga Li
ValueCountFrequency (%)
muhammad 6
 
3.4%
nur 5
 
2.8%
li 3
 
1.7%
zhi 3
 
1.7%
nabilah 3
 
1.7%
ying 3
 
1.7%
binti 3
 
1.7%
lee 2
 
1.1%
chin 2
 
1.1%
nurul 2
 
1.1%
Other values (133) 146
82.0%
2025-05-19T03:28:05.374328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 119
 
12.6%
114
 
12.1%
i 91
 
9.7%
n 76
 
8.1%
h 67
 
7.1%
u 42
 
4.5%
e 39
 
4.1%
r 30
 
3.2%
o 30
 
3.2%
m 28
 
3.0%
Other values (39) 305
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 119
 
12.6%
114
 
12.1%
i 91
 
9.7%
n 76
 
8.1%
h 67
 
7.1%
u 42
 
4.5%
e 39
 
4.1%
r 30
 
3.2%
o 30
 
3.2%
m 28
 
3.0%
Other values (39) 305
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 119
 
12.6%
114
 
12.1%
i 91
 
9.7%
n 76
 
8.1%
h 67
 
7.1%
u 42
 
4.5%
e 39
 
4.1%
r 30
 
3.2%
o 30
 
3.2%
m 28
 
3.0%
Other values (39) 305
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 119
 
12.6%
114
 
12.1%
i 91
 
9.7%
n 76
 
8.1%
h 67
 
7.1%
u 42
 
4.5%
e 39
 
4.1%
r 30
 
3.2%
o 30
 
3.2%
m 28
 
3.0%
Other values (39) 305
32.4%

Gender
Categorical

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Female
47 
Male
20 

Length

Max length6
Median length6
Mean length5.4029851
Min length4

Characters and Unicode

Total characters362
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 47
70.1%
Male 20
29.9%

Length

2025-05-19T03:28:05.530412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-19T03:28:05.641071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 47
70.1%
male 20
29.9%

Most occurring characters

ValueCountFrequency (%)
e 114
31.5%
a 67
18.5%
l 67
18.5%
F 47
13.0%
m 47
13.0%
M 20
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 114
31.5%
a 67
18.5%
l 67
18.5%
F 47
13.0%
m 47
13.0%
M 20
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 114
31.5%
a 67
18.5%
l 67
18.5%
F 47
13.0%
m 47
13.0%
M 20
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 114
31.5%
a 67
18.5%
l 67
18.5%
F 47
13.0%
m 47
13.0%
M 20
 
5.5%

Age
Real number (ℝ)

Distinct18
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.970149
Minimum18
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:05.743847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q122
median26
Q329
95-th percentile34
Maximum39
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.7799143
Coefficient of variation (CV)0.18405417
Kurtosis-0.51979072
Mean25.970149
Median Absolute Deviation (MAD)4
Skewness0.49523902
Sum1740
Variance22.84758
MonotonicityNot monotonic
2025-05-19T03:28:05.857350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20 8
11.9%
21 6
9.0%
22 6
9.0%
31 6
9.0%
26 6
9.0%
27 5
 
7.5%
24 5
 
7.5%
25 4
 
6.0%
29 4
 
6.0%
34 4
 
6.0%
Other values (8) 13
19.4%
ValueCountFrequency (%)
18 1
 
1.5%
20 8
11.9%
21 6
9.0%
22 6
9.0%
23 3
 
4.5%
24 5
7.5%
25 4
6.0%
26 6
9.0%
27 5
7.5%
28 3
 
4.5%
ValueCountFrequency (%)
39 1
 
1.5%
35 1
 
1.5%
34 4
6.0%
33 2
 
3.0%
32 1
 
1.5%
31 6
9.0%
30 1
 
1.5%
29 4
6.0%
28 3
4.5%
27 5
7.5%

Race
Categorical

Distinct4
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Malay
31 
Chinese
30 
Others
India
 
1

Length

Max length7
Median length6
Mean length5.9701493
Min length5

Characters and Unicode

Total characters400
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st rowMalay
2nd rowMalay
3rd rowOthers
4th rowMalay
5th rowChinese

Common Values

ValueCountFrequency (%)
Malay 31
46.3%
Chinese 30
44.8%
Others 5
 
7.5%
India 1
 
1.5%

Length

2025-05-19T03:28:05.999736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-19T03:28:06.125367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
malay 31
46.3%
chinese 30
44.8%
others 5
 
7.5%
india 1
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 65
16.2%
a 63
15.8%
h 35
8.8%
s 35
8.8%
M 31
7.8%
l 31
7.8%
y 31
7.8%
i 31
7.8%
n 31
7.8%
C 30
7.5%
Other values (5) 17
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 65
16.2%
a 63
15.8%
h 35
8.8%
s 35
8.8%
M 31
7.8%
l 31
7.8%
y 31
7.8%
i 31
7.8%
n 31
7.8%
C 30
7.5%
Other values (5) 17
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 65
16.2%
a 63
15.8%
h 35
8.8%
s 35
8.8%
M 31
7.8%
l 31
7.8%
y 31
7.8%
i 31
7.8%
n 31
7.8%
C 30
7.5%
Other values (5) 17
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 65
16.2%
a 63
15.8%
h 35
8.8%
s 35
8.8%
M 31
7.8%
l 31
7.8%
y 31
7.8%
i 31
7.8%
n 31
7.8%
C 30
7.5%
Other values (5) 17
 
4.2%

Tooth
Categorical

Distinct3
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
38
38 
48
28 
20
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters134
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row38
2nd row38
3rd row38
4th row48
5th row48

Common Values

ValueCountFrequency (%)
38 38
56.7%
48 28
41.8%
20 1
 
1.5%

Length

2025-05-19T03:28:06.252121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-19T03:28:06.356802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38 38
56.7%
48 28
41.8%
20 1
 
1.5%

Most occurring characters

ValueCountFrequency (%)
8 66
49.3%
3 38
28.4%
4 28
20.9%
2 1
 
0.7%
0 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 66
49.3%
3 38
28.4%
4 28
20.9%
2 1
 
0.7%
0 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 66
49.3%
3 38
28.4%
4 28
20.9%
2 1
 
0.7%
0 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 66
49.3%
3 38
28.4%
4 28
20.9%
2 1
 
0.7%
0 1
 
0.7%

Impaction
Categorical

Imbalance 

Distinct4
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2A
57 
2B
1A
 
3
1B
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters134
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row1A
2nd row2A
3rd row2A
4th row2A
5th row2A

Common Values

ValueCountFrequency (%)
2A 57
85.1%
2B 6
 
9.0%
1A 3
 
4.5%
1B 1
 
1.5%

Length

2025-05-19T03:28:06.474443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-19T03:28:06.580994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2a 57
85.1%
2b 6
 
9.0%
1a 3
 
4.5%
1b 1
 
1.5%

Most occurring characters

ValueCountFrequency (%)
2 63
47.0%
A 60
44.8%
B 7
 
5.2%
1 4
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 63
47.0%
A 60
44.8%
B 7
 
5.2%
1 4
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 63
47.0%
A 60
44.8%
B 7
 
5.2%
1 4
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 63
47.0%
A 60
44.8%
B 7
 
5.2%
1 4
 
3.0%

Duration
Real number (ℝ)

Distinct25
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.776119
Minimum12
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:06.694419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12.3
Q116
median21
Q325.5
95-th percentile35
Maximum38
Range26
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.0082655
Coefficient of variation (CV)0.32183262
Kurtosis-0.46850633
Mean21.776119
Median Absolute Deviation (MAD)5
Skewness0.6077539
Sum1459
Variance49.115785
MonotonicityNot monotonic
2025-05-19T03:28:06.821426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
22 7
 
10.4%
20 5
 
7.5%
14 5
 
7.5%
35 4
 
6.0%
23 4
 
6.0%
18 4
 
6.0%
12 4
 
6.0%
15 3
 
4.5%
19 3
 
4.5%
21 3
 
4.5%
Other values (15) 25
37.3%
ValueCountFrequency (%)
12 4
6.0%
13 3
4.5%
14 5
7.5%
15 3
4.5%
16 3
4.5%
17 2
 
3.0%
18 4
6.0%
19 3
4.5%
20 5
7.5%
21 3
4.5%
ValueCountFrequency (%)
38 1
 
1.5%
36 1
 
1.5%
35 4
6.0%
34 1
 
1.5%
33 1
 
1.5%
32 2
3.0%
30 1
 
1.5%
29 1
 
1.5%
28 1
 
1.5%
27 3
4.5%

Group
Categorical

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
laser
36 
placebo
31 

Length

Max length7
Median length5
Mean length5.9253731
Min length5

Characters and Unicode

Total characters397
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowplacebo
2nd rowlaser
3rd rowlaser
4th rowlaser
5th rowplacebo

Common Values

ValueCountFrequency (%)
laser 36
53.7%
placebo 31
46.3%

Length

2025-05-19T03:28:06.962272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-19T03:28:07.084506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
laser 36
53.7%
placebo 31
46.3%

Most occurring characters

ValueCountFrequency (%)
l 67
16.9%
a 67
16.9%
e 67
16.9%
s 36
9.1%
r 36
9.1%
p 31
7.8%
c 31
7.8%
b 31
7.8%
o 31
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 67
16.9%
a 67
16.9%
e 67
16.9%
s 36
9.1%
r 36
9.1%
p 31
7.8%
c 31
7.8%
b 31
7.8%
o 31
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 67
16.9%
a 67
16.9%
e 67
16.9%
s 36
9.1%
r 36
9.1%
p 31
7.8%
c 31
7.8%
b 31
7.8%
o 31
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 67
16.9%
a 67
16.9%
e 67
16.9%
s 36
9.1%
r 36
9.1%
p 31
7.8%
c 31
7.8%
b 31
7.8%
o 31
7.8%

VAS (baseline)
Real number (ℝ)

High correlation  Zeros 

Distinct36
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.059701
Minimum0
Maximum89
Zeros10
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:08.561762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13
Q335
95-th percentile74.7
Maximum89
Range89
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.772839
Coefficient of variation (CV)1.0309257
Kurtosis0.37046921
Mean23.059701
Median Absolute Deviation (MAD)13
Skewness1.1201038
Sum1545
Variance565.1479
MonotonicityNot monotonic
2025-05-19T03:28:08.700271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 10
 
14.9%
7 4
 
6.0%
5 4
 
6.0%
26 3
 
4.5%
28 3
 
4.5%
35 3
 
4.5%
12 3
 
4.5%
3 2
 
3.0%
13 2
 
3.0%
37 2
 
3.0%
Other values (26) 31
46.3%
ValueCountFrequency (%)
0 10
14.9%
1 1
 
1.5%
2 2
 
3.0%
3 2
 
3.0%
4 1
 
1.5%
5 4
 
6.0%
6 2
 
3.0%
7 4
 
6.0%
8 2
 
3.0%
11 1
 
1.5%
ValueCountFrequency (%)
89 1
1.5%
78 1
1.5%
76 1
1.5%
75 1
1.5%
74 1
1.5%
71 1
1.5%
65 1
1.5%
61 1
1.5%
54 1
1.5%
51 1
1.5%

VAS Day1
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.283582
Minimum0
Maximum75
Zeros6
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:08.827989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median18
Q335
95-th percentile53.4
Maximum75
Range75
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.068729
Coefficient of variation (CV)0.81085388
Kurtosis0.30794664
Mean22.283582
Median Absolute Deviation (MAD)11
Skewness0.89674777
Sum1493
Variance326.47897
MonotonicityNot monotonic
2025-05-19T03:28:08.963569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
15 6
 
9.0%
0 6
 
9.0%
18 4
 
6.0%
8 4
 
6.0%
12 3
 
4.5%
7 3
 
4.5%
5 3
 
4.5%
38 2
 
3.0%
2 2
 
3.0%
25 2
 
3.0%
Other values (29) 32
47.8%
ValueCountFrequency (%)
0 6
9.0%
2 2
 
3.0%
4 1
 
1.5%
5 3
4.5%
6 1
 
1.5%
7 3
4.5%
8 4
6.0%
9 1
 
1.5%
12 3
4.5%
14 2
 
3.0%
ValueCountFrequency (%)
75 1
1.5%
71 1
1.5%
60 1
1.5%
54 1
1.5%
52 1
1.5%
49 1
1.5%
48 1
1.5%
46 1
1.5%
45 1
1.5%
44 1
1.5%

VAS day2
Real number (ℝ)

High correlation  Zeros 

Distinct33
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.41791
Minimum0
Maximum75
Zeros9
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:09.092238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median11
Q323
95-th percentile52.2
Maximum75
Range75
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation17.723593
Coefficient of variation (CV)1.0175499
Kurtosis2.2101278
Mean17.41791
Median Absolute Deviation (MAD)9
Skewness1.5419465
Sum1167
Variance314.12573
MonotonicityNot monotonic
2025-05-19T03:28:09.235689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 9
 
13.4%
8 6
 
9.0%
6 5
 
7.5%
28 3
 
4.5%
21 3
 
4.5%
9 3
 
4.5%
18 3
 
4.5%
22 3
 
4.5%
4 2
 
3.0%
48 2
 
3.0%
Other values (23) 28
41.8%
ValueCountFrequency (%)
0 9
13.4%
2 2
 
3.0%
3 2
 
3.0%
4 2
 
3.0%
5 2
 
3.0%
6 5
7.5%
7 1
 
1.5%
8 6
9.0%
9 3
 
4.5%
10 1
 
1.5%
ValueCountFrequency (%)
75 1
1.5%
70 1
1.5%
69 1
1.5%
54 1
1.5%
48 2
3.0%
45 1
1.5%
42 1
1.5%
38 1
1.5%
33 1
1.5%
30 1
1.5%

VAS Day7
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8656716
Minimum0
Maximum40
Zeros30
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:09.357497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile17.7
Maximum40
Range40
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.354437
Coefficient of variation (CV)1.5114947
Kurtosis7.3264958
Mean4.8656716
Median Absolute Deviation (MAD)1
Skewness2.3545929
Sum326
Variance54.087743
MonotonicityNot monotonic
2025-05-19T03:28:09.481731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 30
44.8%
8 6
 
9.0%
6 5
 
7.5%
1 4
 
6.0%
3 3
 
4.5%
12 3
 
4.5%
2 3
 
4.5%
4 3
 
4.5%
17 2
 
3.0%
21 1
 
1.5%
Other values (7) 7
 
10.4%
ValueCountFrequency (%)
0 30
44.8%
1 4
 
6.0%
2 3
 
4.5%
3 3
 
4.5%
4 3
 
4.5%
5 1
 
1.5%
6 5
 
7.5%
8 6
 
9.0%
10 1
 
1.5%
12 3
 
4.5%
ValueCountFrequency (%)
40 1
 
1.5%
24 1
 
1.5%
21 1
 
1.5%
18 1
 
1.5%
17 2
 
3.0%
16 1
 
1.5%
13 1
 
1.5%
12 3
4.5%
10 1
 
1.5%
8 6
9.0%

MO (baseline)
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.104478
Minimum36
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:09.611945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile37.3
Q142.5
median45
Q351
95-th percentile57.7
Maximum61
Range25
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.0955382
Coefficient of variation (CV)0.13221141
Kurtosis-0.37525573
Mean46.104478
Median Absolute Deviation (MAD)4
Skewness0.44107283
Sum3089
Variance37.155586
MonotonicityNot monotonic
2025-05-19T03:28:09.739690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
43 10
14.9%
46 5
 
7.5%
38 5
 
7.5%
42 4
 
6.0%
51 4
 
6.0%
44 4
 
6.0%
45 4
 
6.0%
52 4
 
6.0%
47 4
 
6.0%
36 3
 
4.5%
Other values (15) 20
29.9%
ValueCountFrequency (%)
36 3
 
4.5%
37 1
 
1.5%
38 5
7.5%
39 1
 
1.5%
40 1
 
1.5%
41 2
 
3.0%
42 4
 
6.0%
43 10
14.9%
44 4
 
6.0%
45 4
 
6.0%
ValueCountFrequency (%)
61 1
 
1.5%
59 1
 
1.5%
58 2
3.0%
57 1
 
1.5%
56 1
 
1.5%
55 1
 
1.5%
54 1
 
1.5%
53 2
3.0%
52 4
6.0%
51 4
6.0%

MO Day1
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.656716
Minimum18
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:09.861579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21.6
Q126
median33
Q339.5
95-th percentile49.7
Maximum61
Range43
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation9.1891772
Coefficient of variation (CV)0.27302655
Kurtosis0.39730642
Mean33.656716
Median Absolute Deviation (MAD)7
Skewness0.67123215
Sum2255
Variance84.440977
MonotonicityNot monotonic
2025-05-19T03:28:09.984704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
37 6
 
9.0%
26 5
 
7.5%
28 5
 
7.5%
43 5
 
7.5%
35 5
 
7.5%
23 4
 
6.0%
42 3
 
4.5%
40 3
 
4.5%
30 3
 
4.5%
24 3
 
4.5%
Other values (19) 25
37.3%
ValueCountFrequency (%)
18 2
 
3.0%
20 1
 
1.5%
21 1
 
1.5%
23 4
6.0%
24 3
4.5%
25 2
 
3.0%
26 5
7.5%
27 1
 
1.5%
28 5
7.5%
29 2
 
3.0%
ValueCountFrequency (%)
61 1
 
1.5%
56 1
 
1.5%
55 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
46 1
 
1.5%
43 5
7.5%
42 3
4.5%
40 3
4.5%
39 1
 
1.5%

MO Day2
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.373134
Minimum16
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:10.116775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24.3
Q130
median35
Q342.5
95-th percentile53.2
Maximum61
Range45
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation9.1797468
Coefficient of variation (CV)0.25237712
Kurtosis0.068471021
Mean36.373134
Median Absolute Deviation (MAD)7
Skewness0.4615271
Sum2437
Variance84.267752
MonotonicityNot monotonic
2025-05-19T03:28:10.246402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
35 8
 
11.9%
37 5
 
7.5%
28 5
 
7.5%
30 4
 
6.0%
32 4
 
6.0%
26 4
 
6.0%
49 3
 
4.5%
42 3
 
4.5%
43 3
 
4.5%
46 2
 
3.0%
Other values (22) 26
38.8%
ValueCountFrequency (%)
16 1
 
1.5%
20 1
 
1.5%
23 1
 
1.5%
24 1
 
1.5%
25 1
 
1.5%
26 4
6.0%
27 1
 
1.5%
28 5
7.5%
29 1
 
1.5%
30 4
6.0%
ValueCountFrequency (%)
61 1
 
1.5%
57 1
 
1.5%
56 1
 
1.5%
55 1
 
1.5%
49 3
4.5%
48 1
 
1.5%
47 1
 
1.5%
46 2
3.0%
45 2
3.0%
44 1
 
1.5%

MO Day7
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.895522
Minimum18
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:10.366614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile35
Q140.5
median43
Q347
95-th percentile55.4
Maximum61
Range43
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation7.0777807
Coefficient of variation (CV)0.16124152
Kurtosis2.3925473
Mean43.895522
Median Absolute Deviation (MAD)3
Skewness-0.49327728
Sum2941
Variance50.09498
MonotonicityNot monotonic
2025-05-19T03:28:10.490690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
43 10
14.9%
45 8
 
11.9%
52 5
 
7.5%
47 4
 
6.0%
40 4
 
6.0%
42 4
 
6.0%
38 3
 
4.5%
41 3
 
4.5%
46 3
 
4.5%
44 3
 
4.5%
Other values (17) 20
29.9%
ValueCountFrequency (%)
18 1
 
1.5%
26 1
 
1.5%
32 1
 
1.5%
35 2
3.0%
36 3
4.5%
37 1
 
1.5%
38 3
4.5%
39 1
 
1.5%
40 4
6.0%
41 3
4.5%
ValueCountFrequency (%)
61 1
 
1.5%
59 1
 
1.5%
57 1
 
1.5%
56 1
 
1.5%
54 1
 
1.5%
53 1
 
1.5%
52 5
7.5%
51 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%

S1 (baseline)
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.46269
Minimum92
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:10.612081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98.6
Q1105.5
median111
Q3116
95-th percentile127.4
Maximum132
Range40
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation8.3925081
Coefficient of variation (CV)0.075294328
Kurtosis0.21996081
Mean111.46269
Median Absolute Deviation (MAD)6
Skewness0.29463562
Sum7468
Variance70.434193
MonotonicityNot monotonic
2025-05-19T03:28:10.748880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
110 7
 
10.4%
103 6
 
9.0%
112 6
 
9.0%
120 6
 
9.0%
113 4
 
6.0%
108 4
 
6.0%
115 4
 
6.0%
105 3
 
4.5%
107 3
 
4.5%
104 3
 
4.5%
Other values (16) 21
31.3%
ValueCountFrequency (%)
92 1
 
1.5%
93 1
 
1.5%
98 2
 
3.0%
100 1
 
1.5%
103 6
9.0%
104 3
4.5%
105 3
4.5%
106 1
 
1.5%
107 3
4.5%
108 4
6.0%
ValueCountFrequency (%)
132 1
 
1.5%
131 1
 
1.5%
128 2
 
3.0%
126 1
 
1.5%
125 1
 
1.5%
124 1
 
1.5%
120 6
9.0%
118 2
 
3.0%
117 2
 
3.0%
115 4
6.0%

S1 day1
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.28358
Minimum98
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:10.876332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile103
Q1110
median112
Q3118.5
95-th percentile128
Maximum132
Range34
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation7.8215049
Coefficient of variation (CV)0.069043587
Kurtosis-0.10452802
Mean113.28358
Median Absolute Deviation (MAD)6
Skewness0.38627451
Sum7590
Variance61.175938
MonotonicityNot monotonic
2025-05-19T03:28:11.014549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
110 13
19.4%
120 7
10.4%
115 5
 
7.5%
113 5
 
7.5%
118 4
 
6.0%
105 4
 
6.0%
112 4
 
6.0%
103 4
 
6.0%
108 2
 
3.0%
104 2
 
3.0%
Other values (13) 17
25.4%
ValueCountFrequency (%)
98 2
 
3.0%
100 1
 
1.5%
103 4
 
6.0%
104 2
 
3.0%
105 4
 
6.0%
107 1
 
1.5%
108 2
 
3.0%
110 13
19.4%
111 2
 
3.0%
112 4
 
6.0%
ValueCountFrequency (%)
132 1
 
1.5%
131 1
 
1.5%
130 1
 
1.5%
128 2
 
3.0%
126 1
 
1.5%
124 1
 
1.5%
122 2
 
3.0%
120 7
10.4%
119 1
 
1.5%
118 4
6.0%

S1 day2
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.10448
Minimum98
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:11.145433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile103
Q1108
median113
Q3118
95-th percentile127.7
Maximum132
Range34
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.9698947
Coefficient of variation (CV)0.070464891
Kurtosis-0.18344126
Mean113.10448
Median Absolute Deviation (MAD)5
Skewness0.37597073
Sum7578
Variance63.519222
MonotonicityNot monotonic
2025-05-19T03:28:11.277001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
110 8
 
11.9%
115 7
 
10.4%
113 6
 
9.0%
120 5
 
7.5%
103 4
 
6.0%
108 4
 
6.0%
105 4
 
6.0%
98 3
 
4.5%
118 3
 
4.5%
122 3
 
4.5%
Other values (15) 20
29.9%
ValueCountFrequency (%)
98 3
 
4.5%
103 4
6.0%
104 2
 
3.0%
105 4
6.0%
106 1
 
1.5%
107 2
 
3.0%
108 4
6.0%
110 8
11.9%
111 3
 
4.5%
112 2
 
3.0%
ValueCountFrequency (%)
132 1
 
1.5%
131 1
 
1.5%
130 1
 
1.5%
128 1
 
1.5%
127 1
 
1.5%
126 1
 
1.5%
124 1
 
1.5%
123 1
 
1.5%
122 3
4.5%
120 5
7.5%

S1 day7
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.46269
Minimum92
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:11.402762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98.6
Q1105.5
median110
Q3116
95-th percentile127.4
Maximum132
Range40
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation8.3581359
Coefficient of variation (CV)0.074985954
Kurtosis0.26322382
Mean111.46269
Median Absolute Deviation (MAD)5
Skewness0.30836275
Sum7468
Variance69.858435
MonotonicityNot monotonic
2025-05-19T03:28:11.540810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
110 8
 
11.9%
120 6
 
9.0%
112 6
 
9.0%
103 5
 
7.5%
108 5
 
7.5%
115 4
 
6.0%
105 4
 
6.0%
104 3
 
4.5%
113 3
 
4.5%
118 2
 
3.0%
Other values (16) 21
31.3%
ValueCountFrequency (%)
92 1
 
1.5%
93 1
 
1.5%
98 2
 
3.0%
100 1
 
1.5%
103 5
7.5%
104 3
4.5%
105 4
6.0%
106 1
 
1.5%
107 2
 
3.0%
108 5
7.5%
ValueCountFrequency (%)
132 1
 
1.5%
131 1
 
1.5%
128 2
 
3.0%
126 1
 
1.5%
125 1
 
1.5%
124 1
 
1.5%
120 6
9.0%
118 2
 
3.0%
117 2
 
3.0%
115 4
6.0%

S2 (baseline)
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.04478
Minimum100
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:11.673871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile105
Q1112
median118
Q3120.5
95-th percentile127.7
Maximum136
Range36
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation7.1147155
Coefficient of variation (CV)0.060786271
Kurtosis0.63125607
Mean117.04478
Median Absolute Deviation (MAD)4
Skewness-0.0052810144
Sum7842
Variance50.619177
MonotonicityNot monotonic
2025-05-19T03:28:11.805869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
120 12
17.9%
110 7
 
10.4%
115 7
 
10.4%
118 6
 
9.0%
112 4
 
6.0%
123 3
 
4.5%
122 3
 
4.5%
124 3
 
4.5%
113 2
 
3.0%
100 2
 
3.0%
Other values (15) 18
26.9%
ValueCountFrequency (%)
100 2
 
3.0%
104 1
 
1.5%
105 2
 
3.0%
106 1
 
1.5%
110 7
10.4%
111 1
 
1.5%
112 4
6.0%
113 2
 
3.0%
114 1
 
1.5%
115 7
10.4%
ValueCountFrequency (%)
136 1
 
1.5%
135 1
 
1.5%
130 1
 
1.5%
128 1
 
1.5%
127 1
 
1.5%
126 1
 
1.5%
124 3
4.5%
123 3
4.5%
122 3
4.5%
121 2
3.0%

S2 day1
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.97015
Minimum100
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:11.945228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile107.6
Q1115
median120
Q3125
95-th percentile130
Maximum140
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.5918021
Coefficient of variation (CV)0.063280759
Kurtosis1.0028961
Mean119.97015
Median Absolute Deviation (MAD)5
Skewness-0.033956409
Sum8038
Variance57.635459
MonotonicityNot monotonic
2025-05-19T03:28:12.093227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
120 14
20.9%
115 6
 
9.0%
125 6
 
9.0%
130 5
 
7.5%
118 4
 
6.0%
123 3
 
4.5%
122 3
 
4.5%
100 2
 
3.0%
116 2
 
3.0%
113 2
 
3.0%
Other values (16) 20
29.9%
ValueCountFrequency (%)
100 2
 
3.0%
106 1
 
1.5%
107 1
 
1.5%
109 1
 
1.5%
110 1
 
1.5%
111 1
 
1.5%
112 1
 
1.5%
113 2
 
3.0%
114 2
 
3.0%
115 6
9.0%
ValueCountFrequency (%)
140 1
 
1.5%
138 1
 
1.5%
136 1
 
1.5%
130 5
7.5%
128 2
 
3.0%
126 2
 
3.0%
125 6
9.0%
124 1
 
1.5%
123 3
4.5%
122 3
4.5%

S2 day2
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.95522
Minimum100
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:12.231579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile107.2
Q1115
median120
Q3124.5
95-th percentile130
Maximum138
Range38
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.4537622
Coefficient of variation (CV)0.062137871
Kurtosis0.71936141
Mean119.95522
Median Absolute Deviation (MAD)5
Skewness-0.28565437
Sum8037
Variance55.558571
MonotonicityNot monotonic
2025-05-19T03:28:12.365665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
120 8
 
11.9%
115 8
 
11.9%
122 5
 
7.5%
124 5
 
7.5%
118 4
 
6.0%
127 4
 
6.0%
125 4
 
6.0%
110 3
 
4.5%
130 3
 
4.5%
123 3
 
4.5%
Other values (13) 20
29.9%
ValueCountFrequency (%)
100 2
 
3.0%
106 2
 
3.0%
110 3
 
4.5%
111 1
 
1.5%
112 1
 
1.5%
113 2
 
3.0%
115 8
11.9%
116 2
 
3.0%
117 2
 
3.0%
118 4
6.0%
ValueCountFrequency (%)
138 1
 
1.5%
136 1
 
1.5%
135 1
 
1.5%
130 3
4.5%
128 1
 
1.5%
127 4
6.0%
126 2
 
3.0%
125 4
6.0%
124 5
7.5%
123 3
4.5%

S2 day7
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.13433
Minimum100
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:12.498915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile105
Q1112
median118
Q3121
95-th percentile127.7
Maximum136
Range36
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.1687358
Coefficient of variation (CV)0.061200981
Kurtosis0.54023523
Mean117.13433
Median Absolute Deviation (MAD)4
Skewness-0.026283547
Sum7848
Variance51.390773
MonotonicityNot monotonic
2025-05-19T03:28:12.636163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
120 12
17.9%
110 7
 
10.4%
115 7
 
10.4%
118 5
 
7.5%
122 4
 
6.0%
112 4
 
6.0%
124 3
 
4.5%
113 2
 
3.0%
100 2
 
3.0%
105 2
 
3.0%
Other values (16) 19
28.4%
ValueCountFrequency (%)
100 2
 
3.0%
104 1
 
1.5%
105 2
 
3.0%
106 1
 
1.5%
110 7
10.4%
111 1
 
1.5%
112 4
6.0%
113 2
 
3.0%
114 1
 
1.5%
115 7
10.4%
ValueCountFrequency (%)
136 1
 
1.5%
135 1
 
1.5%
130 1
 
1.5%
128 1
 
1.5%
127 1
 
1.5%
126 1
 
1.5%
125 1
 
1.5%
124 3
4.5%
123 2
3.0%
122 4
6.0%

S3 (baseline)
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.32836
Minimum105
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:12.779388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile122.9
Q1140
median150
Q3155
95-th percentile162.7
Maximum170
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.686653
Coefficient of variation (CV)0.086111409
Kurtosis1.1686582
Mean147.32836
Median Absolute Deviation (MAD)8
Skewness-1.0042223
Sum9871
Variance160.95115
MonotonicityNot monotonic
2025-05-19T03:28:12.915404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
155 13
19.4%
160 5
 
7.5%
140 5
 
7.5%
150 5
 
7.5%
145 4
 
6.0%
130 3
 
4.5%
153 3
 
4.5%
151 3
 
4.5%
142 3
 
4.5%
137 3
 
4.5%
Other values (17) 20
29.9%
ValueCountFrequency (%)
105 1
 
1.5%
115 1
 
1.5%
120 1
 
1.5%
122 1
 
1.5%
125 1
 
1.5%
130 3
4.5%
135 2
 
3.0%
136 1
 
1.5%
137 3
4.5%
140 5
7.5%
ValueCountFrequency (%)
170 1
 
1.5%
165 2
 
3.0%
163 1
 
1.5%
162 2
 
3.0%
160 5
 
7.5%
159 1
 
1.5%
158 1
 
1.5%
155 13
19.4%
154 1
 
1.5%
153 3
 
4.5%

S3 day1
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.10448
Minimum110
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:13.041657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile124.2
Q1144.5
median155
Q3158
95-th percentile166.4
Maximum175
Range65
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation12.827686
Coefficient of variation (CV)0.085458383
Kurtosis1.1604467
Mean150.10448
Median Absolute Deviation (MAD)8
Skewness-0.96566495
Sum10057
Variance164.54953
MonotonicityNot monotonic
2025-05-19T03:28:13.170254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
155 10
 
14.9%
160 5
 
7.5%
140 4
 
6.0%
145 4
 
6.0%
150 3
 
4.5%
165 3
 
4.5%
147 3
 
4.5%
157 3
 
4.5%
158 3
 
4.5%
156 2
 
3.0%
Other values (22) 27
40.3%
ValueCountFrequency (%)
110 1
 
1.5%
115 1
 
1.5%
122 1
 
1.5%
123 1
 
1.5%
127 1
 
1.5%
131 1
 
1.5%
132 2
3.0%
137 1
 
1.5%
140 4
6.0%
141 1
 
1.5%
ValueCountFrequency (%)
175 1
 
1.5%
170 1
 
1.5%
168 1
 
1.5%
167 1
 
1.5%
165 3
4.5%
163 2
 
3.0%
162 1
 
1.5%
160 5
7.5%
159 1
 
1.5%
158 3
4.5%

S3 day2
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.1194
Minimum108
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:13.294405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile125.1
Q1144.5
median153
Q3158
95-th percentile167.1
Maximum174
Range66
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation13.020406
Coefficient of variation (CV)0.086733667
Kurtosis1.179554
Mean150.1194
Median Absolute Deviation (MAD)7
Skewness-0.95667841
Sum10058
Variance169.53098
MonotonicityNot monotonic
2025-05-19T03:28:13.420614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
155 9
 
13.4%
140 5
 
7.5%
160 4
 
6.0%
157 4
 
6.0%
145 3
 
4.5%
148 3
 
4.5%
158 3
 
4.5%
165 3
 
4.5%
151 3
 
4.5%
170 2
 
3.0%
Other values (21) 28
41.8%
ValueCountFrequency (%)
108 1
 
1.5%
115 1
 
1.5%
123 2
 
3.0%
130 2
 
3.0%
132 1
 
1.5%
133 1
 
1.5%
137 2
 
3.0%
138 1
 
1.5%
140 5
7.5%
144 1
 
1.5%
ValueCountFrequency (%)
174 1
 
1.5%
170 2
3.0%
168 1
 
1.5%
165 3
4.5%
164 2
3.0%
163 1
 
1.5%
162 1
 
1.5%
160 4
6.0%
158 3
4.5%
157 4
6.0%

S3 day7
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.32836
Minimum105
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:13.541048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile122.9
Q1140
median150
Q3155
95-th percentile162.7
Maximum170
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.686653
Coefficient of variation (CV)0.086111409
Kurtosis1.1686582
Mean147.32836
Median Absolute Deviation (MAD)8
Skewness-1.0042223
Sum9871
Variance160.95115
MonotonicityNot monotonic
2025-05-19T03:28:13.668450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
155 13
19.4%
160 5
 
7.5%
140 5
 
7.5%
150 5
 
7.5%
145 4
 
6.0%
130 3
 
4.5%
153 3
 
4.5%
151 3
 
4.5%
142 3
 
4.5%
137 3
 
4.5%
Other values (17) 20
29.9%
ValueCountFrequency (%)
105 1
 
1.5%
115 1
 
1.5%
120 1
 
1.5%
122 1
 
1.5%
125 1
 
1.5%
130 3
4.5%
135 2
 
3.0%
136 1
 
1.5%
137 3
4.5%
140 5
7.5%
ValueCountFrequency (%)
170 1
 
1.5%
165 2
 
3.0%
163 1
 
1.5%
162 2
 
3.0%
160 5
 
7.5%
159 1
 
1.5%
158 1
 
1.5%
155 13
19.4%
154 1
 
1.5%
153 3
 
4.5%

Arcoxia (days)
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9701493
Minimum0
Maximum7
Zeros14
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size668.0 B
2025-05-19T03:28:13.781065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5.7
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6964325
Coefficient of variation (CV)0.86106802
Kurtosis0.70122412
Mean1.9701493
Median Absolute Deviation (MAD)1
Skewness0.98851255
Sum132
Variance2.8778833
MonotonicityNot monotonic
2025-05-19T03:28:13.894540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 19
28.4%
1 15
22.4%
0 14
20.9%
3 8
11.9%
4 5
 
7.5%
6 3
 
4.5%
5 2
 
3.0%
7 1
 
1.5%
ValueCountFrequency (%)
0 14
20.9%
1 15
22.4%
2 19
28.4%
3 8
11.9%
4 5
 
7.5%
5 2
 
3.0%
6 3
 
4.5%
7 1
 
1.5%
ValueCountFrequency (%)
7 1
 
1.5%
6 3
 
4.5%
5 2
 
3.0%
4 5
 
7.5%
3 8
11.9%
2 19
28.4%
1 15
22.4%
0 14
20.9%

Interactions

2025-05-19T03:28:01.483773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:26:59.960724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:02.373178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:04.607692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:07.074384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:12.576255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:14.890564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:17.794902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:20.305525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:22.616613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:25.498800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:27.773716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:30.030688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:33.126053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:35.480421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.894619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:41.148261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:43.573444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:46.038505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:48.480543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:50.999471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:54.528661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:56.921101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:59.179257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:01.593219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:00.065081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:02.483808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:04.701403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:07.173453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:12.676348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:14.985174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:17.910238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:20.400644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:22.724243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:25.597800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:27.869902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:30.126273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:33.226654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:35.592774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.996550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:41.251487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:43.676976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:46.144224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-05-19T03:27:24.845817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-05-19T03:27:29.475782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:32.511172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:34.865368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.314074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:39.650630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:42.932787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:45.416228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:47.860572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:50.378133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:53.934021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:56.334052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:58.618398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:00.894163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:03.317805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:01.851502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:04.124658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:06.605686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:12.116894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:14.395158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:17.244634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:19.787495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:22.125534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:24.965493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:27.296210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:29.569864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:32.614260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:34.959363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.414288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:39.757387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:43.039735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:45.520978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:47.958887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:50.480052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:54.032172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:56.437523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:58.718971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:00.998123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:03.418442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:01.954947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:04.219848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:06.708619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:12.210967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:14.495132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:17.360720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:19.890785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:22.225590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:25.065927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:27.387367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:29.662901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:32.718230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:35.063926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.509905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:39.854198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:43.151513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:45.624776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:48.063449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:50.588944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:54.137575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:56.536024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:58.806463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:01.091065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:03.509193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:02.053467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:04.319095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:06.799788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:12.300314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:14.590984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:17.465186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:19.996263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:22.329410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:25.163534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:27.475020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:29.758588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:32.815721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:35.168531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.602686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:39.955983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:43.263647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:45.729995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:48.181011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:50.686213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:54.230257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:56.631381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:58.898179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:01.196754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:03.601334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:02.155796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:04.417281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:06.886374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:12.389842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:14.701621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:17.576967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:20.102830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:22.420389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:25.292403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:27.570109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:29.850117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:32.914957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:35.289162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.701387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:40.064526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:43.364085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:45.825515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:48.285373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:50.785418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:54.325682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:56.733376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:58.988626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:01.287730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:03.700014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:02.262388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:04.510038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:06.981055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:12.484414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:14.799448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:17.687896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:20.199442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:22.514331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:25.390097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:27.672774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:29.934933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:33.011367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:35.382552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:37.795442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:41.041388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:43.469036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:45.926971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:48.384894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:50.891380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:54.429595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:56.826727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:27:59.076783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-05-19T03:28:01.382984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-05-19T03:28:14.016776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeArcoxia (days)DurationGenderGroupImpactionMO (baseline)MO Day1MO Day2MO Day7RaceS1 (baseline)S1 day1S1 day2S1 day7S2 (baseline)S2 day1S2 day2S2 day7S3 (baseline)S3 day1S3 day2S3 day7SubjectToothVAS (baseline)VAS Day1VAS Day7VAS day2
Age1.000-0.029-0.0260.0000.0000.248-0.0740.065-0.031-0.1440.084-0.105-0.077-0.106-0.119-0.104-0.150-0.106-0.1190.0690.0600.0730.069-0.0470.000-0.079-0.099-0.0330.044
Arcoxia (days)-0.0291.0000.2560.0000.3020.0000.047-0.010-0.0910.0480.2690.1830.2030.2220.1830.0520.0570.0840.0570.0510.1320.1180.0510.0020.0000.1900.1390.3030.280
Duration-0.0260.2561.0000.0000.0660.0000.193-0.140-0.1690.0000.263-0.025-0.0020.002-0.025-0.0420.012-0.000-0.045-0.097-0.111-0.117-0.097-0.0440.0000.0560.0610.2710.101
Gender0.0000.0000.0001.0000.0000.2760.2600.2600.3810.2050.0970.3480.3280.3960.3410.3840.1920.2270.3480.4620.4210.4300.4620.0000.0000.1600.0000.0000.000
Group0.0000.3020.0660.0001.0000.0000.0000.2290.2090.2350.0000.0000.0000.1350.0000.1510.0000.0000.1210.0120.0000.2740.0120.0000.0000.0190.1090.0260.320
Impaction0.2480.0000.0000.2760.0001.0000.1580.2950.1470.0000.0000.0810.2220.1600.0690.0000.1990.2300.0000.2780.1450.2400.2780.2360.0000.3300.0000.0000.000
MO (baseline)-0.0740.0470.1930.2600.0000.1581.0000.2480.3640.8220.0000.1200.1400.1290.1480.1680.1710.1320.1670.2260.1750.1690.226-0.0830.000-0.079-0.091-0.146-0.178
MO Day10.065-0.010-0.1400.2600.2290.2950.2481.0000.8150.4670.0000.1690.1280.0970.1500.124-0.0080.0240.1100.1240.0310.0500.1240.0920.039-0.134-0.221-0.128-0.224
MO Day2-0.031-0.091-0.1690.3810.2090.1470.3640.8151.0000.6080.1890.1530.1120.0650.1420.1740.0700.0330.1590.066-0.008-0.0130.0660.0490.060-0.115-0.232-0.209-0.345
MO Day7-0.1440.0480.0000.2050.2350.0000.8220.4670.6081.0000.2460.2240.2370.2060.2400.2420.2520.2180.2430.2500.1850.1620.2500.0300.000-0.065-0.084-0.144-0.155
Race0.0840.2690.2630.0970.0000.0000.0000.0000.1890.2461.0000.0000.0000.0000.0000.0000.1020.1860.0000.0000.0000.0000.0000.0460.0000.1220.1840.0000.358
S1 (baseline)-0.1050.183-0.0250.3480.0000.0810.1200.1690.1530.2240.0001.0000.9430.9500.9950.5160.4710.5000.5060.2370.2670.2590.2370.2280.0000.028-0.0340.0510.091
S1 day1-0.0770.203-0.0020.3280.0000.2220.1400.1280.1120.2370.0000.9431.0000.9800.9460.4990.5010.5240.4950.2540.2800.2780.2540.2350.1600.002-0.0240.1410.127
S1 day2-0.1060.2220.0020.3960.1350.1600.1290.0970.0650.2060.0000.9500.9801.0000.9550.5060.5090.5470.5050.2750.3130.3080.2750.2520.0000.0420.0100.1130.150
S1 day7-0.1190.183-0.0250.3410.0000.0690.1480.1500.1420.2400.0000.9950.9460.9551.0000.5280.4780.4930.5200.2520.2820.2750.2520.2430.0000.036-0.0220.0620.107
S2 (baseline)-0.1040.052-0.0420.3840.1510.0000.1680.1240.1740.2420.0000.5160.4990.5060.5281.0000.9020.8650.9960.5550.5440.5480.5550.1980.2270.0470.0810.1480.180
S2 day1-0.1500.0570.0120.1920.0000.1990.171-0.0080.0700.2520.1020.4710.5010.5090.4780.9021.0000.9660.9040.5160.5310.5130.5160.2190.2450.1360.2170.1840.232
S2 day2-0.1060.084-0.0000.2270.0000.2300.1320.0240.0330.2180.1860.5000.5240.5470.4930.8650.9661.0000.8670.5050.5170.5060.5050.2050.2070.1220.2150.1650.263
S2 day7-0.1190.057-0.0450.3480.1210.0000.1670.1100.1590.2430.0000.5060.4950.5050.5200.9960.9040.8671.0000.5590.5550.5560.5590.2050.2430.0590.0880.1560.188
S3 (baseline)0.0690.051-0.0970.4620.0120.2780.2260.1240.0660.2500.0000.2370.2540.2750.2520.5550.5160.5050.5591.0000.9570.9661.0000.1830.0000.0240.044-0.0070.167
S3 day10.0600.132-0.1110.4210.0000.1450.1750.031-0.0080.1850.0000.2670.2800.3130.2820.5440.5310.5170.5550.9571.0000.9850.9570.1600.0000.0660.098-0.0120.216
S3 day20.0730.118-0.1170.4300.2740.2400.1690.050-0.0130.1620.0000.2590.2780.3080.2750.5480.5130.5060.5560.9660.9851.0000.9660.1340.0000.0740.0890.0080.205
S3 day70.0690.051-0.0970.4620.0120.2780.2260.1240.0660.2500.0000.2370.2540.2750.2520.5550.5160.5050.5591.0000.9570.9661.0000.1830.0000.0240.044-0.0070.167
Subject-0.0470.002-0.0440.0000.0000.236-0.0830.0920.0490.0300.0460.2280.2350.2520.2430.1980.2190.2050.2050.1830.1600.1340.1831.0000.1930.019-0.0540.1850.105
Tooth0.0000.0000.0000.0000.0000.0000.0000.0390.0600.0000.0000.0000.1600.0000.0000.2270.2450.2070.2430.0000.0000.0000.0000.1931.0000.0000.0000.0000.000
VAS (baseline)-0.0790.1900.0560.1600.0190.330-0.079-0.134-0.115-0.0650.1220.0280.0020.0420.0360.0470.1360.1220.0590.0240.0660.0740.0240.0190.0001.0000.6480.2480.440
VAS Day1-0.0990.1390.0610.0000.1090.000-0.091-0.221-0.232-0.0840.184-0.034-0.0240.010-0.0220.0810.2170.2150.0880.0440.0980.0890.044-0.0540.0000.6481.0000.3810.679
VAS Day7-0.0330.3030.2710.0000.0260.000-0.146-0.128-0.209-0.1440.0000.0510.1410.1130.0620.1480.1840.1650.156-0.007-0.0120.008-0.0070.1850.0000.2480.3811.0000.565
VAS day20.0440.2800.1010.0000.3200.000-0.178-0.224-0.345-0.1550.3580.0910.1270.1500.1070.1800.2320.2630.1880.1670.2160.2050.1670.1050.0000.4400.6790.5651.000

Missing values

2025-05-19T03:28:03.868856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-19T03:28:04.236668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SubjectNameGenderAgeRaceToothImpactionDurationGroupVAS (baseline)VAS Day1VAS day2VAS Day7MO (baseline)MO Day1MO Day2MO Day7S1 (baseline)S1 day1S1 day2S1 day7S2 (baseline)S2 day1S2 day2S2 day7S3 (baseline)S3 day1S3 day2S3 day7Arcoxia (days)
01Muhammad Amerul AkmalMale27Malay381A35placebo183827355404045989898981001001001001051101081054
12Sarah NabihahFemale28Malay382A28laser2861462934429310098931001001001001151151151152
23Pan AnyuFemale25Others382A27laser01200502341451051051051051121131131121301321331300
34Afiqah bitni Abdul MalikFemale26Malay482A23laser01571474345471001101051001121151151121401401401402
45Tiang Nga LiFemale20Chinese482A18placebo7750493745471071071071071121121121121401451461403
56Koay Shir JinFemale27Chinese482A33laser35404518482428451081101081081301401351301401401401401
67Sahyro Safuan IzzatMale27Malay382A27placebo893580472328401101101101101201281261201551581581552
78King Ee VeeFemale24Chinese382A25placebo0000432727401051101081051171171171171551551551551
89Nur Athirah DayiniFemale22Malay482A23laser65757010433029431251281271251161181181161541561561543
910Leong Lai MunFemale22Chinese482A35laser35603017462831351101131131101241261241241551601601552
SubjectNameGenderAgeRaceToothImpactionDurationGroupVAS (baseline)VAS Day1VAS day2VAS Day7MO (baseline)MO Day1MO Day2MO Day7S1 (baseline)S1 day1S1 day2S1 day7S2 (baseline)S2 day1S2 day2S2 day7S3 (baseline)S3 day1S3 day2S3 day7Arcoxia (days)
5758Khin Sandar MyintFemale32Others482A22laser7000434343431171171171171151151151151431451441430
5859NabilahFemale23Malay482A20placebo2818168373735371031031031031191221221191451471481450
5960On Chian RoeiFemale34Chinese482A14placebo14122454244451081111081081111141111111511511511511
6061Nurul Fatin AbdullahFemale20Malay482A32placebo54272821501825451031101101051181221201201551601571552
6162Aireen NajwaFemale22Malay382A17laser764890383535381041041041041121151151121501501521500
6263Chua Yih ThinkFemale33Chinese382A12laser3760484348481131151131131201201201201551551551551
6364ShameerMale31Malay382A19placebo617158453537451201221231201231251271251551571571553
6465Nur QaisaraFemale18Malay382A23placebo1215215433735431201201201201211231251211251271301254
6566Goh Shu JieMale21Chinese482A22laser261486454042451311311311311231251241231221221231221
6667Muhammad AmzarMale22Malay482A24placebo626248433335431281301301281221251251221201231231204